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Multimorbidity and major adverse cardiovascular events in antipsychotic users: Time-to-event prediction by explainable machine learning.

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Title: Multimorbidity and major adverse cardiovascular events in antipsychotic users: Time-to-event prediction by explainable machine learning.
Authors: Sun Q; Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China., Liu W; Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China., Wei C; Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China., Hu Y; Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China., Zhou L; Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China., Liu B; Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China., Chu RYK; Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China., Song S; Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.; Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Sha Tin, Hong Kong, China., Tian W; Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China., Chan EWY; Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.; Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Sha Tin, Hong Kong, China., Chan SKW; Department of Psychiatry, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China., Tsoi KKF; JC School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China.; Stanley Ho Big Data Decision Analytics Research Centre, The Chinese University of Hong Kong, Hong Kong, China., Wong ICK; Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.; Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Sha Tin, Hong Kong, China.; Advanced Data Analytics for Medical Science (ADAMS) Limited, Hong Kong, China.; Aston Pharmacy School, Aston University Birmingham, Birmingham, UK., Osborn DPJ; Division of Psychiatry, University College London, London, UK., Smith D; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK., Lai FTT; Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.; Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Sha Tin, Hong Kong, China.; Advanced Data Analytics for Medical Science (ADAMS) Limited, Hong Kong, China.; Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
Source: IScience [iScience] 2026 Apr 03; Vol. 29 (5), pp. 115586. Date of Electronic Publication: 2026 Apr 03 (Print Publication: 2026).
Publication Type: Journal Article
Journal Info: Publisher: Cell Press Country of Publication: United States NLM ID: 101724038 Publication Model: eCollection Cited Medium: Internet ISSN: 2589-0042 (Electronic) Linking ISSN: 25890042 NLM ISO Abbreviation: iScience Subsets: PubMed not MEDLINE
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